• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.355-362
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• 2012

Remaining useful life(RUL) prediction of a system is important in the prognostics field since it is directly linked with safety and maintenance scheduling. In the physics-based prognostics, accurately estimated model parameters can predict the remaining useful life exactly. It, however, is not a simple task to estimate the model parameters because most real system have multivariate model parameters, also they are correlated each other. This paper presents representative methods to estimate model parameters in the physics-based prognostics and discusses the difference between three methods; the particle filter method(PF), the overall Bayesian method(OBM), and the sequential Bayesian method(SBM). The three methods are based on the same theoretical background, the Bayesian estimation technique, but the methods are distinguished from each other in the sampling methods or uncertainty analysis process. Therefore, a simple physical model as an easy task and the Paris model for crack growth problem are used to discuss the difference between the three methods, and the performance of each method evaluated by using established prognostics metrics is compared.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.29-38
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• 2011

In order to derive domain-independent heuristics from the specification of a planning problem, it is required to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems with both uncertainty about the initial state and non-deterministic action effects. The merged planning graph is an extended one to be applied to the contingent planning problems from the relaxed planning graph, which is a common means to get effective heuristics for solving the classical planning problems. In order to get heuristics for solving the contingent planning problems with sensing actions and non-deterministic actions, the new graph utilizes additionally the effect-merge relaxations of these actions as well as the traditional delete relaxations. Proceeding parallel to the forward expansion of the merged planning graph, the computation of GD heuristic excludes the unnecessary redundant cost from estimating the minimal reachability cost to achieve the overall set of goals by analyzing interdependencies among goals or subgoals. Therefore, GD heuristics have the advantage that they usually require less computation time than the overlap heuristics, but are more informative than the max and the additive heuristics. In this paper, we explain the experimental analysis to show the accuracy and the search efficiency of the GD heuristics.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.155-167
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• 2017

Accurate QPE (Quantitative Precipitation Estimation) and the quality of the rainfall data for hydrological analysis are very important factors. Especially, the quality has a great influence on flood runoff result. It needs to know characteristics of the uncertainties in radar QPE for the reliable flood analysis. The purpose of this study is to present a probabilistic approach which defines the range of possible values or probabilistic distributions rather than a single value to consider the uncertainties in radar QPE and evaluate its applicability by applying it to radar rainfall. This study generated radar rainfall ensemble for the storms by the typhoon 'Sanba' on Namgang dam basin, Korea. It was shown that the rainfall ensemble is able to simulate well the pattern of the rain-gauge rainfall as well as to correct well the overall bias of the radar rainfall. The suggested ensemble technique represented well the uncertainties of radar QPE. As a result, the rainfall ensemble model by a probabilistic approach can provide various rainfall scenarios which is a useful information for a decision making such as flood forecasting and warning.

• Journal of Korea Water Resources Association
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• v.50 no.11
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• pp.769-779
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• 2017

As the occurrence of drought is recently on the rise, the reliable drought forecasting is required for developing the drought mitigation and proactive management of water resources. This study developed a probabilistic hydrological drought forecasting method using the Bayesian Networks and drought propagation relationship to estimate future drought with the forecast uncertainty, named as the Propagated Bayesian Networks Drought Forecasting (PBNDF) model. The proposed PBNDF model was composed with 4 nodes of past, current, multi-model ensemble (MME) forecasted information and the drought propagation relationship. Using Palmer Hydrological Drought Index (PHDI), the PBNDF model was applied to forecast the hydrological drought condition at 10 gauging stations in Nakdong River basin. The receiver operating characteristics (ROC) curve analysis was applied to measure the forecast skill of the forecast mean values. The root mean squared error (RMSE) and skill score (SS) were employed to compare the forecast performance with previously developed forecast models (persistence forecast, Bayesian network drought forecast). We found that the forecast skill of PBNDF model showed better performance with low RMSE and high SS of 0.1~0.15. The overall results mean the PBNDF model had good potential in probabilistic drought forecasting.

• Journal of Climate Change Research
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• v.9 no.1
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• pp.13-29
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• 2018

This study presented evaluation procedure for selecting appropriate GCMs and downscaling method by focusing on the climate extreme indices suitable for climate change adaptation. The procedure includes six stages of processes as follows: 1) exclusion of unsuitable GCM through raw GCM analysis before bias correction; 2) calculation of the climate extreme indices and selection of downscaling method by evaluating reproducibility for the past and distortion rate for the future period; 3) selection of downscaling method based on evaluation of reproducibility of spatial correlation among weather stations; and 4) MME calculation using weight factors and evaluation of uncertainty range depending on number of GCMs. The presented procedure was applied to 60 weather stations where there are observed data for the past 30 year period on Korea Peninsula. First, 22 GCMs were selected through the evaluation of the spatio-temporal reproducibility of 29 GCMs. Between Simple Quantile Mapping (SQM) and Spatial Disaggregation Quantile Delta Mapping (SDQDM) methods, SQM was selected based on the reproducibility of 27 climate extreme indices for the past and reproducibility evaluation of spatial correlation in precipitation and temperature. Total precipitation (prcptot) and annual 1-day maximum precipitation (rx1day), which is respectively related to water supply and floods, were selected and MME-based future projections were estimated for near-future (2010-2039), the mid-future (2040-2069), and the far-future (2070-2099) based on the weight factors by GCM. The prcptot and rx1day increased as time goes farther from the near-future to the far-future and RCP 8.5 showed a higher rate of increase in both indices compared to RCP 4.5 scenario. It was also found that use of 20 GCM out of 22 explains 80% of the overall variation in all combinations of RCP scenarios and future periods. The result of this study is an example of an application in Korea Peninsula and APCC Integrated Modeling Solution (AIMS) can be utilized in various areas and fields if users want to apply the proposed procedure directly to a target area.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.263-270
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• 2021

In this study, a reliability-based design optimization of a 130-m class fixed-type offshore platform, to be installed in the North Sea, was carried out, while considering environmental, material, and manufacturing uncertainties to enhance its structural safety and economic aspects. For the reliability analysis, and reliability-based design optimization of the structural integrity, unity check values (defined as the ratio between working and allowable stress, for axial, bending, and shear stresses), of the members of the offshore platform were considered as constraints. Weight of the supporting jacket structure was minimized to reduce the manufacturing cost of the offshore platform. Statistical characteristics of uncertainties were defined based on observed and measured data references. Reliability analysis and reliability-based design optimization of a jacket-type offshore structure were computationally burdensome due to the large number of members; therefore, we suggested a method for variable screening, based on the importance of their output responses, to reduce the dimension of the problem. Furthermore, a deterministic design optimization was carried out prior to the reliability-based design optimization, to improve overall computational efficiency. Finally, the optimal design obtained was compared with the conventional rule-based offshore platform design in terms of safety and cost.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1101-1117
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• 2018

The clear sky radiance (CSR) is one of the baseline products of the Himawari-8 which was launched on October, 2014. The CSR contributes to numerical weather prediction (NWP) accuracy through the data assimilation; especially water vapor channel CSR has good impact on the forecast in high level atmosphere. The focus of this study is the quality analysis of the CSR of the Himawari-8 geostationary satellite. We used the operational CSR (or clear sky brightness temperature) products in JMA (Japan Meteorological Agency) as observation data; for a background field, we employed the CSR simulated using the Radiative Transfer for TOVS (RTTOV) with the atmospheric state from the global model of KMA (Korea Meteorological Administration). We investigated data characteristics and analyzed observation minus background statistics of each channel with respect to regional and seasonal variability. Overall results for the analysis period showed that the water vapor channels (6.2, 6.9, and $7.3{\mu}m$) had a positive mean bias where as the window channels(10.4, 11.2, and $12.4{\mu}m$) had a negative mean bias. The magnitude of biases and Uncertainty result varied with the regional and the seasonal conditions, thus these should be taken into account when using CSR data. This study is helpful for the pre-processing of Himawari-8/Advanced Himawari Imager (AHI) CSR data assimilation. Furthermore, this study also can contribute to preparing for the utilization of products from the Geo-Kompsat-2A (GK-2A), which will be launched in 2018 by the National Meteorological Satellite Center (NMSC) of KMA.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• Korean small business review
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• v.43 no.1
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• pp.1-33
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• 2021

This study explores differential value implications of R&D expenditure across firms, especially in terms of growth potential of small businesses. Analyzing Korean listed firms for the period from 1982 to 2014, we document the followings. First, large firms, defined as the top quintile group based on market capitalization, have spent higher R&D expenditure compared to small (bottom quintile group) and medium (middle quintile groups) firms and the difference between groups has enlarged over time. Relatedly, the persistence of R&D spending, measured by the association between current R&D expenditure and cumulative future R&D expenditure over the next five years, is lowest in small firms. Second, R&D of large (small) firms are more (less) likely to generate operating profits over the next five years. Additional analyses suggest that the relation between R&D and gross margin is strongest in large firms, suggesting that R&D underlies their competitiveness in the product market. Third, small firms have borne the highest uncertainty related to R&D investment proxied by the association between current R&D and volatility of future earnings. As a result, the likelihood of R&D leading to future patents is also lowest in small firms. Fourth, the probability of moving up to the next size group within the next five years is significantly lower in small firms than others. Finally, we find that the divergence in R&D expenditure between large and small firms is positively associated with product market concentration. Overall, our findings confirm the small business growth trap in relation to R&D investment.